Forest fires cause significant economic damage and hazard to
environment all over the world. Apart from preventive measures, early warning and fast
extinction of fires are the only chance to avoid major casualties and damage to nature,
especially in regions with dense population. As a common method, trained staff observes
the endangered areas. In Germany alone, several hundred observation towers were erected in
the forests. The staff works up to 12 hours per day and usually under difficult
circumstances (extreme temperatures, isolation, continuous concentration).

To date, all attempts to develop a technical system for this task have
failed to outlast the test stage. In most cases the chosen components do not work reliable
enough. The Autonomous Early Warning System For Forest Fires AWFS described herein is
based on new concepts of hard- and software. It is adapted to the specific conditions in
densely wooded regions and detects fire by the trail of smoke.

AWFS consists of a rotating digital camera with a special filter and an
innovative electronic system. Thus, an utmost high level of reliability is achieved. The
noise is extremely low and allows high radiometric resolution (14bit). Digital data are
transmitted from the camera to the computer via optical fibers and get evaluated. The
necessary software forms the central component of the system. It recognizes smoke almost
in real time by analyzing its typical dynamic and stochastic features. This became
possible by modifying know-how gained in space projects. However, only recent development
of fast CPUs and high capacity storage media allowed to finally solve complex problems of
real-time picture processing at low cost. Warnings are autonomously passed over to a
central unit, where an operator will evaluate them. For this purpose, comprehensive and
user-optimized software was developed. It visualizes all information necessary for taking
further steps and assists in decision-making.

AWFS was installed and tested on three observation towers in Brandenburg,
Germany, during the 1999 forest fire season. It became apparent that the main requirement
for absolute reliable smoke detection was met. The false alarm rate due to weather and
harvest activities commonly remained below 1%, which is well acceptable. Other
improvements will be effective soon. The testing forest authority confirmed that the
system is mature for service and easy to use. The number of systems will be increased
therefore. Moreover, other German states and some European countries are very interested
in this technology.

Introduction

The probability of fires in forests and fields is steadily increasing due
to climate changes and human activities. In Europe, up to 10,000 km² of vegetation are
destroyed by fire every year, and even 20,000 to 90,000 km² in North America. Prognoses
presume from the assumption that forest fires including fire clearing in tropical rain
forests will halve the worlds forest stand by 2030. Vegetation fires result in high
human death toll, speed up the extinction of species and worsen the greenhouse effect.
Approximately 20 % of the CO2 emissions into the atmosphere are released by
forest fires, estimated the Enquiry commission "Prevention To Save The Earth
Atmosphere" of the German Bundestag in 1990. Germany, too, will be affected by the
impacts of global climate change. In Brandenburg and some other German states enormous
economic damage is caused by forest fires. For instance, in Brandenburg alone more than
1,000 forest fires are registered every year. 90 % of them are caused by human activities.
Large fires cause a total damage of up to 70,000 DM/ha. The annual financial loss caused
by forest fires in Germany amounts to a two-digit number of millions. However, preventive
and fire extinction measures cost even several times this sum.

In order to minimize damage, forest fires must be recognized as soon as
possible (within a few minutes). Therefore, great efforts are made in all respective
regions to achieve early recognition. Although numerous technical methods were tested,
reliability was in no case sufficient to develop a product suitable for the German market.
As an example, infrared sensor systems tested in Spain can only detect the fire itself.
However, smoke is the feature relevant for early recognition of fires in densely wooded
areas. Optical systems as AWIS in the Netherlands (Breejen et al. 1998) and Firehawk in
South Africa are also in a test phase. But they often imply a high rate of false alarm
caused by clouds, light reflection, agricultural activities and industrial plants.

In Canada and Russia an early warning system based on aircraft patrolling
is used, which means late recognition of forest fires though and is expensive to operate.
Evaluating satellite data is also not very successful, as spatial and time resolution are
not sufficient to allow local prevention. Moreover, clouds obstruct the view very often. .
However, with the German project BIRD a new generation of imaging infrared sensors for
Earth remote sensing objectives including is developed (Briess et al. 1999). A major
intention of the BIRD is to demonstrate the scientific and technological value and the
technical and programmatic feasibility of fire detection of under low-budget constraints.
The start of this small satellite mission is planned for the end of year 2000. Within the
European project FUEGO an operational mini satellite
constellation is studied (Gonzalo 1996.). It will become operational in
2004 to provide early fire outbreak detection and high resolution fire-line monitoring. Hence
to date experienced fire-watchers are employed everywhere in the world to observe
endangered forests. In Germany several hundred observation towers are manned during main
forest fire season. The fire-watchers observe the forests up to 12 hours per day under
utmost difficult circumstances (extreme temperatures, awkward hygiene conditions,
isolation, only short breaks from concentration) and report about any smoke formation.
Apart from that, authorities usually have to spent large sums on the construction of
observation towers, as these edifices need to be built, maintained and operated in
accordance with relevant legislation and regulation. As an example, approximately DM
350,000 are required to build one observation tower in Brandenburg.

The pilot project "Autonomous Early Warning System For Forest
Fires" (AWFS) was ordered and supported by the forest authority of Peitz,
Brandenburg, and promoted by the European Union. It comprised installation and testing of
a system for the following tasks:

A solution for this complex undertaking was found by further developing
know-how from unmanned space missions and consistently adapting it to the problem of
forest fire recognition.

Technical description of AWFS

According to the forest authoritys specification, an autonomous
early warning system for forest fires must meet the following technical requirements:

Automatically recognize smoke formation of 10 m expansion by daylight within a radius of
10 km and within 10 minutes after becoming visible

High reliability in respect of fire recognition

Acceptable rate of false alarm

Localize the source of the fire

Easy maintenance

Autonomous transmission of smoke data to a control center

Full record-keeping of all events

Data transmission to control center must enable the operator to independently evaluate
the potential hazard

System concept

Principally, there are various methods suitable to recognize vegetation
fires, e.g. analyzing picture information provided by digital cameras or by infrared
imagers, or detecting emission lines of conflagration gases, or active measurements with
Lidar evaluating the laser signal backscattered from smoke particles. To find out which
method is suited best, several preliminary tests were performed observing controlled fires
with various sensors (CCD-camera, IR-radiometer, IR-spectrometer). We had to take into
account our own results as well as experience gained internationally on the field of early
forest fire recognition, also given facts on site in Germany, the technical requirements
mentioned above and the required user-friendliness and economic efficiency. Bearing all
this in mind, we chose a sensor type based on a digital CCD-camera with high resolution.
Smoke detection within the visible spectral region is especially important in densely
wooded forests, as open flames (which IR-sensors respond to) give alarm too late.
Furthermore, cameras provide the operator in the control center with expressive images and
hence make it easier for him to evaluate the situation. It was one of the projects
main objectives to allow human contribution in a suitable way during the process of
evaluating the alarm and selecting the appropriate fire fighting method. For such purpose,
the control center is equipped with a number of computer-assisted supports.

Fig.1. Digital CCD-camera

The digital high resolution Frame Transfer CCD-camera with special filter
(see Figure 1) scans the forests from the top of the observation tower. AWFS can also be
mounted to braced poles of mobile phone providers, high buildings or other suitable
locations. The images are resolved with 14 bits and transmitted via optical fibers to the
computer unit which is located in the tower too. There they get analyzed by means of
specially developed software. If there seems to be a smoke formation, compressed pictures
and further details (time, position) are reported via ISDN to the control center, where
they are processed in a PC displayed on the screen. The operator receives all information
he needs to make decisions. Currently, one control center can support up to 7 towers. In
each tower up to 8000 digital images with a data volume of 16 GByte are produced and
evaluated every day.

Hardware components installed in the observation tower

The basic components of AWFS are shown in Fig.2. The most distinctive
feature of the CCD-camera is its innovative electronic concept of four functional groups:
CCD-head, clock-driver, analogue signal chain and controller. The camera is mounted on the
very top of the tower by means of a pan and tilt unit (PTU). It takes the camera
approximately 10 minutes to come full circle. The controller generates or manages all
digital control signals for the CCD transport cycles, analog signal processing and
PC-interface. Incoming commands are interpreted and carried out. The video signal is
pre-processed in the signal chain on analog basis, then submitted to correlated double
sampling, before it runs through further conditioning and multi-level filter. After the
signal is digitalized in a 14bit analog-to-digital converter the image data are serialized
and transmitted to the controlling PC via optical fibers.

Fig.2.Basic components of AWFS

The electronic components are utmost resistant against environmental
conditions, stand out for their low energy consumption and extremely low noise. Due to the
high radiometric resolution (~16,000 different grey scale values) the camera covers a wide
range. Even very structures can be resolved under all sorts of lighting conditions. The 70
mm objective with 10° field of view allows 2 m geometric resolution in 10 km distance.
Tests confirmed that the red-free filter increases the contrast between vegetation and
smoke, as red light is hardly reflected at all by chlorophyll.

The pan and tilt unit can be positioned with a relative precision of up to
0.2° and with an absolute precision of 1° after being oriented in the landscape by means
of GPS-defined land marks. During scanning stage three single images are taken in 1-second
intervals for every camera position. Then, full image information is transmitted to a
controlling PC at the tower bottom, where the data are evaluated, stored and passed on to
an image processing computer. Both computers work with the operating system MS Windows NT.

Moreover, the controlling PC covers the following functions:

Autonomous control of camera image taking

Autonomous control of the pan and tilt unit

Compressing alarm images before transmitting them

Control of alarm data transmission (time, location of smoke etc.) to the operator in the
control center

Data transmission to the PC in the control center is currently achieved by
a wired ISDN-connection. However, it is also possible do use radio transmission or other
specific networks.

Image processing software

The image processing computer uses complex algorithms to identify smoke in
real time. Simultaneously, it calculates the optimum exposure time and sends it to the
controlling PC. The image processing software is the heart of the AWFS. It evaluates in
only a few seconds typical features like dynamic and stochastic behaviour. In every camera
position several images are taken. First of all, exact matching must be achieved for the
images taken from the same camera position, because the towers tend to swing considerably
in the wind. Next, the horizon line is determined in the image for orientation purposes.
The smoke is identified then by means of dynamic and structural features and by its grey
scale value. It is prerequisite to reliable recognition that several features are taken
into consideration.

In a first step, typical features of smoke are looked for by analyzing a
standard difference image. Wind and thermal convection of hot smoke gases change the grey
scale value of smoke areas in the subsequent images. However, other environmental
phenomena (e.g. clouds, wind, dust formations, reflections, bird flights, cars) can cause
similar effects for short periods of time (in comparison with the time scale of the
dynamic behaviour of smoke). Moving objects (cars, planes, birds) can be eliminated by an
additional evaluation of the third image, because smoke is quite stationary within the
time between first and last image (several seconds). Irrelevant space frequencies are
eliminated by band-pass filtering the standard difference image. As the special red filter
reduces the green colour of the forest significantly, smoke of wood fire always stands out
against the surrounding forest. This fact supports additional suppression of interfering
signals (as an example, smoke is easy to distinct from cloud shadow therefore. Finally,
adaptable threshold values prompt the decision, whether fire alarm is to be released or
not.

In a second step, the texture is evaluated. The respective method is based
on the structural analysis of the texture of smoke, which can be clearly discerned from
the surrounding structures. It works even without any comparison image and hence does not
react to changes in illumination nor to moving objects like vehicles. From mathematical
view, the structures are described as stochastic effects superimposed to the average grey
scale value. Therefore, it is first necessary to calculate the estimated average grey
scale value, so that the stochastic arises from the difference between the original and
the estimated image. The mathematical basis is explained by Hetzheim (1999). Typical smoke
structures are separated then by means of various procedures. A second image, which is
prerequisite to the first step described above, are reused to verify the results.

In accordance with the observed features both methods proceed classing the
identified possible smoke areas with probabilities. These are condensed to one total
probability then. As an example, the total probability is low, if the identified areas do
not overlap and clearly differ in size.

Each of the two methods described above works sufficiently enough to
detect smoke on its own, but their simultaneous employment increases the reliability of
smoke detection considerably.

Control Center

The control center, too, is equipped with a PC and appropriate software.
It deals with the following tasks:

Control of several camera locations

Receive alarm images and data

Visualize alarm images in a suitable way and in correlation with digital maps

Display a low resolution panorama view of all towers administrated by the center

Manual area definition in order to permanently mask smoke of irrelevant origin
(chimneys, villages, etc.)

Provide the operator with tools for adequate evaluation of images (zoom, image
sequences, filters, facilities to change contrast and brightness)

Display the bearing lines of alarm messages on digital maps

Fade-in additional information and data bases in accordance with the users
requirements

Fig.3. Fire detection from Kathlow tower on 8 August 1999

By means of the software developed for the control center, even operators
who might not be familiar with modern PC-technology can easily make themselves acquainted
with their scope of duties within a few days. Their knowledge about local conditions as
well as the information the system provides them with enable them to soon make qualified
decisions on initiation of fire fighting activities. Annex 2 shows a possible display
variant on the monitor.

Results and discussion

Only practical operation can demonstrate the performance of an autonomous
early-warning system for forest fires. Therefore, a pilot test was started during the
forest fire season 1999, after several tests with controlled fire were made. Supplementary
to traditional fire watching methods, AWFS were installed on three observation towers
(Kathlow, Reuthen, Jerischke) of the Spree-Neiße district in southern Brandenburg, which
is a region with very high forest fire risk. The numerous open pits and power plants in
this region with their dust and smoke emissions make the task of fire watching especially
difficult. The control center is located at the premises of the local forest authority in
Peitz. The test results were monthly evaluated and reported in cooperation with the
responsible officers.

In the test region 16 forest and field fires happened. All these fires
were detected and indicated by the AWFS within the set time limit. Despite the
approximately 10 min time of revolution, fire indication sometimes (especially during late
afternoon) was given even earlier than by the experienced observation tower staff, who
obviously suffered from symptoms of tiredness. Figure 3 is an example of automatic smoke
detection.

False alarms mean a specific problem. One differentiates between alarm due
to irrelevant smoke sources, e.g. chimneys, and proper false alarm. Irrelevant smoke
sources are usually stationary objects. Therefore, a facility was created, that allows the
operator to permanently mask these sources. The complex image analysis during which
various features are evaluated in a multi-step process has been described above and proven
to efficiently avoid potential false alarms.

Figure 4 presents the statistical evaluation of the false alarm rates at
all three pilot project locations during the period from 16/8/1999 to 18/9/1999 (end of
forest fire season). For most days the rate of false alarm is clearly less than 1%. About
230 decisions about smoke formations are to be made hourly by the software on each tower.
A rate of 1 % means approximately 2 false alarms per hour, which the operator can easily
cope with. However, under certain weather conditions the number of false alarms increases
due to light reflection, ascending water vapor (after short but heavy rain) or low clouds.
Dust formation as a result of harvest activities can be taken for smoke, too. Even
experienced staff has often considerable problems to differentiate properly though. The
region around Kathlow has higher rates, which is due to Cottbus city and the Jänschwalde
open pit and power plant being in the range of view of the camera. The option to mask such
smoke sources was not entirely used yet in the pilot project.

Fig.4. Number of days with different rates of false alarm at the
different AWFS locations from 16 August to 18 September 1999

Any problems occurring were continuously evaluated during the test stage
and the software improved accordingly. As a result, the user was satisfied with the AWFS.

All in all, AWFS offers the following advantages:

Reliable and flexible observation of regions with high forest fire risk, prepared for
service at any time of the day

Omission of jobs with difficult working conditions, creation of new jobs in equipment
production and maintenance as well as in the control center

No need for observation towers in forest regions, low costs for installation and
maintenance (braced poles of mobile phone providers)

Further development

The test-stage turned out to be successful. The experience gained will be
considered and evaluated during the next few months. A new generation of AWFS will be
developed and tested.

Here are the focal points of future development:

The computers of several towers will be concluded in a direct network, so they will be
able to control the operation of each other (watchdog-system) and hence further increase
reliability in service

High performance PCs will be used, which will lead to lower turn-around times

False alarm rates will be still reduced by further development and optimization of the
software, making use of the comprehensive image data base

Reliability of smoke detection will be further improved by means of a neuronal
algorithms for classification

Due to its universal basic structure, AWFS can be used in other areas
as well. The concept of the system (digital camera with high spatial and radiometric
resolution, wide range in brightness, real-time image processing, autonomous alarm signal
transmission to a control center) and the experience gained so far in detecting complex
structures in natural environment are suitable for various observation tasks, e.g.
environmental monitoring or security duties. The system can not only observe sensitive
areas, but also autonomously give alarm, transfer data to any other place and selectively
store images.